Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations111733
Missing cells0
Missing cells (%)0.0%
Duplicate rows2526
Duplicate rows (%)2.3%
Total size in memory41.8 MiB
Average record size in memory392.1 B

Variable types

Text1
Numeric9
Categorical16

Alerts

Dataset has 2526 (2.3%) duplicate rowsDuplicates
AverageLeadTime is highly overall correlated with BookingsCheckedIn and 4 other fieldsHigh correlation
BookingsCheckedIn is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
DistributionChannel is highly overall correlated with MarketSegmentHigh correlation
LodgingRevenue is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
MarketSegment is highly overall correlated with DistributionChannelHigh correlation
OtherRevenue is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
PersonsNights is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
RoomNights is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
BookingsNoShowed is highly imbalanced (99.7%) Imbalance
DistributionChannel is highly imbalanced (57.8%) Imbalance
SRHighFloor is highly imbalanced (74.6%) Imbalance
SRLowFloor is highly imbalanced (98.6%) Imbalance
SRAccessibleRoom is highly imbalanced (99.7%) Imbalance
SRMediumFloor is highly imbalanced (99.1%) Imbalance
SRBathtub is highly imbalanced (96.9%) Imbalance
SRShower is highly imbalanced (98.3%) Imbalance
SRCrib is highly imbalanced (88.1%) Imbalance
SRNearElevator is highly imbalanced (99.6%) Imbalance
SRAwayFromElevator is highly imbalanced (96.6%) Imbalance
SRNoAlcoholInMiniBar is highly imbalanced (99.7%) Imbalance
SRQuietRoom is highly imbalanced (57.1%) Imbalance
BookingsCanceled is highly skewed (γ1 = 84.06919629) Skewed
BookingsCheckedIn is highly skewed (γ1 = 26.42580106) Skewed
AverageLeadTime has 36678 (32.8%) zeros Zeros
LodgingRevenue has 33769 (30.2%) zeros Zeros
OtherRevenue has 33552 (30.0%) zeros Zeros
BookingsCanceled has 111567 (99.9%) zeros Zeros
BookingsCheckedIn has 33198 (29.7%) zeros Zeros
PersonsNights has 33202 (29.7%) zeros Zeros
RoomNights has 33198 (29.7%) zeros Zeros

Reproduction

Analysis started2025-03-02 17:23:50.763020
Analysis finished2025-03-02 17:24:07.320011
Duration16.56 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Distinct199
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
2025-03-02T17:24:07.834448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters335199
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowDEU
4th rowFRA
5th rowFRA
ValueCountFrequency (%)
fra 16516
14.8%
deu 14805
13.3%
prt 14101
12.6%
gbr 11462
10.3%
esp 6123
 
5.5%
usa 5409
 
4.8%
ita 4268
 
3.8%
bel 4111
 
3.7%
bra 4037
 
3.6%
nld 3794
 
3.4%
Other values (189) 27107
24.3%
2025-03-02T17:24:08.285038image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 54928
16.4%
A 36750
11.0%
E 30432
9.1%
U 25951
 
7.7%
P 21940
 
6.5%
T 21072
 
6.3%
B 20220
 
6.0%
D 19948
 
6.0%
F 17548
 
5.2%
S 16964
 
5.1%
Other values (16) 69446
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 335199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 54928
16.4%
A 36750
11.0%
E 30432
9.1%
U 25951
 
7.7%
P 21940
 
6.5%
T 21072
 
6.3%
B 20220
 
6.0%
D 19948
 
6.0%
F 17548
 
5.2%
S 16964
 
5.1%
Other values (16) 69446
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 335199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 54928
16.4%
A 36750
11.0%
E 30432
9.1%
U 25951
 
7.7%
P 21940
 
6.5%
T 21072
 
6.3%
B 20220
 
6.0%
D 19948
 
6.0%
F 17548
 
5.2%
S 16964
 
5.1%
Other values (16) 69446
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 335199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 54928
16.4%
A 36750
11.0%
E 30432
9.1%
U 25951
 
7.7%
P 21940
 
6.5%
T 21072
 
6.3%
B 20220
 
6.0%
D 19948
 
6.0%
F 17548
 
5.2%
S 16964
 
5.1%
Other values (16) 69446
20.7%

Age
Real number (ℝ)

Distinct106
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.690002
Minimum-10
Maximum123
Zeros18
Zeros (%)< 0.1%
Negative14
Negative (%)< 0.1%
Memory size1.7 MiB
2025-03-02T17:24:08.416688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile15
Q134
median47
Q357
95-th percentile73
Maximum123
Range133
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.921899
Coefficient of variation (CV)0.37036327
Kurtosis-0.27383267
Mean45.690002
Median Absolute Deviation (MAD)12
Skewness-0.16493941
Sum5105081
Variance286.35066
MonotonicityNot monotonic
2025-03-02T17:24:08.544772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 6450
 
5.8%
51 2582
 
2.3%
52 2561
 
2.3%
55 2557
 
2.3%
50 2504
 
2.2%
54 2493
 
2.2%
48 2458
 
2.2%
49 2441
 
2.2%
53 2430
 
2.2%
56 2360
 
2.1%
Other values (96) 82897
74.2%
ValueCountFrequency (%)
-10 2
 
< 0.1%
-9 4
 
< 0.1%
-8 2
 
< 0.1%
-6 3
 
< 0.1%
-5 3
 
< 0.1%
0 18
 
< 0.1%
1 141
0.1%
2 231
0.2%
3 193
0.2%
4 217
0.2%
ValueCountFrequency (%)
123 1
 
< 0.1%
115 2
< 0.1%
114 3
< 0.1%
111 2
< 0.1%
110 1
 
< 0.1%
97 1
 
< 0.1%
95 1
 
< 0.1%
93 4
< 0.1%
92 2
< 0.1%
91 3
< 0.1%

DaysSinceCreation
Real number (ℝ)

Distinct1349
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean595.0266
Minimum36
Maximum1385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-02T17:24:08.673861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile80
Q1288
median522
Q3889
95-th percentile1263.4
Maximum1385
Range1349
Interquartile range (IQR)601

Descriptive statistics

Standard deviation374.65738
Coefficient of variation (CV)0.62964812
Kurtosis-0.97159451
Mean595.0266
Median Absolute Deviation (MAD)295
Skewness0.39956274
Sum66484107
Variance140368.15
MonotonicityNot monotonic
2025-03-02T17:24:08.826099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 298
 
0.3%
522 247
 
0.2%
108 234
 
0.2%
312 234
 
0.2%
137 232
 
0.2%
571 227
 
0.2%
391 225
 
0.2%
485 220
 
0.2%
368 211
 
0.2%
507 211
 
0.2%
Other values (1339) 109394
97.9%
ValueCountFrequency (%)
36 6
 
< 0.1%
37 109
0.1%
38 68
 
0.1%
39 200
0.2%
40 113
0.1%
41 140
0.1%
42 141
0.1%
43 101
0.1%
44 106
0.1%
45 98
0.1%
ValueCountFrequency (%)
1385 70
0.1%
1384 90
0.1%
1383 103
0.1%
1382 16
 
< 0.1%
1381 99
0.1%
1380 21
 
< 0.1%
1379 10
 
< 0.1%
1378 15
 
< 0.1%
1377 5
 
< 0.1%
1376 20
 
< 0.1%

AverageLeadTime
Real number (ℝ)

High correlation  Zeros 

Distinct424
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.833147
Minimum-1
Maximum588
Zeros36678
Zeros (%)32.8%
Negative13
Negative (%)< 0.1%
Memory size1.7 MiB
2025-03-02T17:24:08.970864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median21
Q395
95-th percentile236
Maximum588
Range589
Interquartile range (IQR)95

Descriptive statistics

Standard deviation85.11532
Coefficient of variation (CV)1.3991602
Kurtosis4.3838783
Mean60.833147
Median Absolute Deviation (MAD)21
Skewness1.9213556
Sum6797070
Variance7244.6177
MonotonicityNot monotonic
2025-03-02T17:24:09.117619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36678
32.8%
1 2121
 
1.9%
2 1271
 
1.1%
6 1257
 
1.1%
4 1224
 
1.1%
3 1193
 
1.1%
5 1191
 
1.1%
7 1160
 
1.0%
8 1083
 
1.0%
9 823
 
0.7%
Other values (414) 63732
57.0%
ValueCountFrequency (%)
-1 13
 
< 0.1%
0 36678
32.8%
1 2121
 
1.9%
2 1271
 
1.1%
3 1193
 
1.1%
4 1224
 
1.1%
5 1191
 
1.1%
6 1257
 
1.1%
7 1160
 
1.0%
8 1083
 
1.0%
ValueCountFrequency (%)
588 19
< 0.1%
574 10
< 0.1%
549 22
< 0.1%
546 10
< 0.1%
543 2
 
< 0.1%
542 5
 
< 0.1%
541 5
 
< 0.1%
535 22
< 0.1%
534 1
 
< 0.1%
533 2
 
< 0.1%

LodgingRevenue
Real number (ℝ)

High correlation  Zeros 

Distinct12689
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.85128
Minimum0
Maximum21781
Zeros33769
Zeros (%)30.2%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-02T17:24:09.244394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median208
Q3393.3
95-th percentile882
Maximum21781
Range21781
Interquartile range (IQR)393.3

Descriptive statistics

Standard deviation379.13156
Coefficient of variation (CV)1.3356697
Kurtosis149.30816
Mean283.85128
Median Absolute Deviation (MAD)208
Skewness6.1687755
Sum31715555
Variance143740.74
MonotonicityNot monotonic
2025-03-02T17:24:09.356253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33769
30.2%
176 988
 
0.9%
126 673
 
0.6%
234 592
 
0.5%
264 551
 
0.5%
249 525
 
0.5%
168 489
 
0.4%
89 405
 
0.4%
178 331
 
0.3%
210 325
 
0.3%
Other values (12679) 73085
65.4%
ValueCountFrequency (%)
0 33769
30.2%
18 2
 
< 0.1%
22 1
 
< 0.1%
24 5
 
< 0.1%
25 1
 
< 0.1%
28 1
 
< 0.1%
34 4
 
< 0.1%
35 1
 
< 0.1%
36 2
 
< 0.1%
37 1
 
< 0.1%
ValueCountFrequency (%)
21781 1
< 0.1%
14044.8 1
< 0.1%
9682.4 1
< 0.1%
9665.66 1
< 0.1%
9180 1
< 0.1%
9010 1
< 0.1%
8493.65 1
< 0.1%
7902 1
< 0.1%
7458 1
< 0.1%
7256 1
< 0.1%

OtherRevenue
Real number (ℝ)

High correlation  Zeros 

Distinct5338
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.682802
Minimum0
Maximum8859.25
Zeros33552
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-02T17:24:09.487426image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31
Q384
95-th percentile235
Maximum8859.25
Range8859.25
Interquartile range (IQR)84

Descriptive statistics

Standard deviation123.58071
Coefficient of variation (CV)1.9105653
Kurtosis578.66804
Mean64.682802
Median Absolute Deviation (MAD)31
Skewness14.895075
Sum7227203.5
Variance15272.193
MonotonicityNot monotonic
2025-03-02T17:24:09.617094image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33552
30.0%
42 3030
 
2.7%
14 2913
 
2.6%
28 2328
 
2.1%
56 1723
 
1.5%
7 1717
 
1.5%
21 1311
 
1.2%
16 1120
 
1.0%
2 1089
 
1.0%
8 1045
 
0.9%
Other values (5328) 61905
55.4%
ValueCountFrequency (%)
0 33552
30.0%
1 370
 
0.3%
1.9 1
 
< 0.1%
2 1089
 
1.0%
2.1 3
 
< 0.1%
2.2 2
 
< 0.1%
2.4 1
 
< 0.1%
2.5 3
 
< 0.1%
3 184
 
0.2%
3.24 1
 
< 0.1%
ValueCountFrequency (%)
8859.25 1
 
< 0.1%
5268.5 1
 
< 0.1%
5261 1
 
< 0.1%
5237 7
< 0.1%
5105.5 1
 
< 0.1%
4296 1
 
< 0.1%
3692.4 1
 
< 0.1%
3580.5 1
 
< 0.1%
3190.4 1
 
< 0.1%
3050.85 1
 
< 0.1%

BookingsCanceled
Real number (ℝ)

Skewed  Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0022822264
Minimum0
Maximum15
Zeros111567
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-02T17:24:09.725537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08063146
Coefficient of variation (CV)35.330176
Kurtosis12061.368
Mean0.0022822264
Median Absolute Deviation (MAD)0
Skewness84.069196
Sum255
Variance0.0065014323
MonotonicityNot monotonic
2025-03-02T17:24:09.808495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 111567
99.9%
1 125
 
0.1%
2 19
 
< 0.1%
3 11
 
< 0.1%
4 8
 
< 0.1%
15 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 111567
99.9%
1 125
 
0.1%
2 19
 
< 0.1%
3 11
 
< 0.1%
4 8
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
4 8
 
< 0.1%
3 11
 
< 0.1%
2 19
 
< 0.1%
1 125
 
0.1%
0 111567
99.9%

BookingsNoShowed
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
111676 
1
 
48
2
 
8
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Length

2025-03-02T17:24:09.909459image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:10.029849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

BookingsCheckedIn
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73760662
Minimum0
Maximum76
Zeros33198
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-02T17:24:10.125275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.73088936
Coefficient of variation (CV)0.99089317
Kurtosis1943.6126
Mean0.73760662
Median Absolute Deviation (MAD)0
Skewness26.425801
Sum82415
Variance0.53419925
MonotonicityNot monotonic
2025-03-02T17:24:10.234153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 76474
68.4%
0 33198
29.7%
2 1634
 
1.5%
3 201
 
0.2%
4 57
 
0.1%
5 33
 
< 0.1%
7 30
 
< 0.1%
6 19
 
< 0.1%
9 13
 
< 0.1%
8 12
 
< 0.1%
Other values (23) 62
 
0.1%
ValueCountFrequency (%)
0 33198
29.7%
1 76474
68.4%
2 1634
 
1.5%
3 201
 
0.2%
4 57
 
0.1%
5 33
 
< 0.1%
6 19
 
< 0.1%
7 30
 
< 0.1%
8 12
 
< 0.1%
9 13
 
< 0.1%
ValueCountFrequency (%)
76 1
< 0.1%
66 1
< 0.1%
40 1
< 0.1%
38 1
< 0.1%
35 1
< 0.1%
32 1
< 0.1%
29 2
< 0.1%
26 2
< 0.1%
25 1
< 0.1%
24 1
< 0.1%

PersonsNights
Real number (ℝ)

High correlation  Zeros 

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3283184
Minimum0
Maximum116
Zeros33202
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-02T17:24:10.361699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q36
95-th percentile12
Maximum116
Range116
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6307386
Coefficient of variation (CV)1.0698701
Kurtosis12.871917
Mean4.3283184
Median Absolute Deviation (MAD)4
Skewness2.0002975
Sum483616
Variance21.44374
MonotonicityNot monotonic
2025-03-02T17:24:10.491180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33202
29.7%
6 16328
14.6%
4 12926
 
11.6%
2 11815
 
10.6%
8 10245
 
9.2%
1 5147
 
4.6%
3 4964
 
4.4%
10 4296
 
3.8%
12 3931
 
3.5%
9 2253
 
2.0%
Other values (50) 6626
 
5.9%
ValueCountFrequency (%)
0 33202
29.7%
1 5147
 
4.6%
2 11815
 
10.6%
3 4964
 
4.4%
4 12926
 
11.6%
5 1116
 
1.0%
6 16328
14.6%
7 291
 
0.3%
8 10245
 
9.2%
9 2253
 
2.0%
ValueCountFrequency (%)
116 1
< 0.1%
99 1
< 0.1%
91 1
< 0.1%
80 1
< 0.1%
75 1
< 0.1%
68 2
< 0.1%
62 1
< 0.1%
60 1
< 0.1%
59 1
< 0.1%
57 1
< 0.1%

RoomNights
Real number (ℝ)

High correlation  Zeros 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2038252
Minimum0
Maximum185
Zeros33198
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-02T17:24:10.619968image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile6
Maximum185
Range185
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3016373
Coefficient of variation (CV)1.0443829
Kurtosis489.93256
Mean2.2038252
Median Absolute Deviation (MAD)2
Skewness9.1896303
Sum246240
Variance5.2975341
MonotonicityNot monotonic
2025-03-02T17:24:10.856486image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 33198
29.7%
3 20706
18.5%
2 17484
15.6%
4 14050
12.6%
1 13665
12.2%
5 6248
 
5.6%
7 2570
 
2.3%
6 2424
 
2.2%
8 505
 
0.5%
9 271
 
0.2%
Other values (39) 612
 
0.5%
ValueCountFrequency (%)
0 33198
29.7%
1 13665
12.2%
2 17484
15.6%
3 20706
18.5%
4 14050
12.6%
5 6248
 
5.6%
6 2424
 
2.2%
7 2570
 
2.3%
8 505
 
0.5%
9 271
 
0.2%
ValueCountFrequency (%)
185 1
< 0.1%
116 1
< 0.1%
95 1
< 0.1%
88 2
< 0.1%
59 1
< 0.1%
51 2
< 0.1%
49 1
< 0.1%
48 1
< 0.1%
42 2
< 0.1%
40 2
< 0.1%

DistributionChannel
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Travel Agent/Operator
91019 
Direct
16883 
Corporate
 
3135
GDS Systems
 
696

Length

Max length21
Median length21
Mean length18.334494
Min length6

Characters and Unicode

Total characters2048568
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate
2nd rowTravel Agent/Operator
3rd rowTravel Agent/Operator
4th rowTravel Agent/Operator
5th rowTravel Agent/Operator

Common Values

ValueCountFrequency (%)
Travel Agent/Operator 91019
81.5%
Direct 16883
 
15.1%
Corporate 3135
 
2.8%
GDS Systems 696
 
0.6%

Length

2025-03-02T17:24:10.981638image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:11.091186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
travel 91019
44.7%
agent/operator 91019
44.7%
direct 16883
 
8.3%
corporate 3135
 
1.5%
gds 696
 
0.3%
systems 696
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 296210
14.5%
e 293771
14.3%
t 202752
 
9.9%
a 185173
 
9.0%
o 97289
 
4.7%
p 94154
 
4.6%
91715
 
4.5%
T 91019
 
4.4%
/ 91019
 
4.4%
O 91019
 
4.4%
Other values (14) 514447
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2048568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 296210
14.5%
e 293771
14.3%
t 202752
 
9.9%
a 185173
 
9.0%
o 97289
 
4.7%
p 94154
 
4.6%
91715
 
4.5%
T 91019
 
4.4%
/ 91019
 
4.4%
O 91019
 
4.4%
Other values (14) 514447
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2048568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 296210
14.5%
e 293771
14.3%
t 202752
 
9.9%
a 185173
 
9.0%
o 97289
 
4.7%
p 94154
 
4.6%
91715
 
4.5%
T 91019
 
4.4%
/ 91019
 
4.4%
O 91019
 
4.4%
Other values (14) 514447
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2048568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 296210
14.5%
e 293771
14.3%
t 202752
 
9.9%
a 185173
 
9.0%
o 97289
 
4.7%
p 94154
 
4.6%
91715
 
4.5%
T 91019
 
4.4%
/ 91019
 
4.4%
O 91019
 
4.4%
Other values (14) 514447
25.1%

MarketSegment
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
Other
63680 
Direct
16363 
Travel Agent/Operator
16353 
Groups
11461 
Corporate
 
2931
Other values (2)
 
945

Length

Max length21
Median length5
Mean length7.7504497
Min length5

Characters and Unicode

Total characters865981
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate
2nd rowTravel Agent/Operator
3rd rowTravel Agent/Operator
4th rowTravel Agent/Operator
5th rowTravel Agent/Operator

Common Values

ValueCountFrequency (%)
Other 63680
57.0%
Direct 16363
 
14.6%
Travel Agent/Operator 16353
 
14.6%
Groups 11461
 
10.3%
Corporate 2931
 
2.6%
Complementary 657
 
0.6%
Aviation 288
 
0.3%

Length

2025-03-02T17:24:11.200768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:11.333894image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
other 63680
49.7%
direct 16363
 
12.8%
travel 16353
 
12.8%
agent/operator 16353
 
12.8%
groups 11461
 
8.9%
corporate 2931
 
2.3%
complementary 657
 
0.5%
aviation 288
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 147082
17.0%
e 133347
15.4%
t 116625
13.5%
O 80033
9.2%
h 63680
 
7.4%
a 36582
 
4.2%
o 34621
 
4.0%
p 31402
 
3.6%
n 17298
 
2.0%
l 17010
 
2.0%
Other values (15) 188301
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 865981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 147082
17.0%
e 133347
15.4%
t 116625
13.5%
O 80033
9.2%
h 63680
 
7.4%
a 36582
 
4.2%
o 34621
 
4.0%
p 31402
 
3.6%
n 17298
 
2.0%
l 17010
 
2.0%
Other values (15) 188301
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 865981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 147082
17.0%
e 133347
15.4%
t 116625
13.5%
O 80033
9.2%
h 63680
 
7.4%
a 36582
 
4.2%
o 34621
 
4.0%
p 31402
 
3.6%
n 17298
 
2.0%
l 17010
 
2.0%
Other values (15) 188301
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 865981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 147082
17.0%
e 133347
15.4%
t 116625
13.5%
O 80033
9.2%
h 63680
 
7.4%
a 36582
 
4.2%
o 34621
 
4.0%
p 31402
 
3.6%
n 17298
 
2.0%
l 17010
 
2.0%
Other values (15) 188301
21.7%

SRHighFloor
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
106983 
1
 
4750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

Length

2025-03-02T17:24:11.447542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:11.550637image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

SRLowFloor
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
111587 
1
 
146

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

Length

2025-03-02T17:24:11.632148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:11.737552image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

SRAccessibleRoom
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
111708 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

Length

2025-03-02T17:24:11.818768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:11.919791image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

SRMediumFloor
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
111647 
1
 
86

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

Length

2025-03-02T17:24:12.004831image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:12.101933image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

SRBathtub
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
111383 
1
 
350

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

Length

2025-03-02T17:24:12.186116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:12.288589image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

SRShower
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
111551 
1
 
182

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

Length

2025-03-02T17:24:12.371639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:12.471066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

SRCrib
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
109925 
1
 
1808

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

Length

2025-03-02T17:24:12.555235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:12.672323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

SRKingSizeBed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
71144 
1
40589 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

Length

2025-03-02T17:24:12.755686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:12.853838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

Most occurring characters

ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

SRTwinBed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
94212 
1
17521 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

Length

2025-03-02T17:24:12.944101image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:13.046013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

SRNearElevator
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
111696 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

Length

2025-03-02T17:24:13.134220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:13.237963image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

SRAwayFromElevator
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
111331 
1
 
402

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

Length

2025-03-02T17:24:13.319737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:13.417933image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

SRNoAlcoholInMiniBar
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
111711 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

Length

2025-03-02T17:24:13.498790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:13.599597image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

SRQuietRoom
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
0
101932 
1
 
9801

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Length

2025-03-02T17:24:13.695908image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-02T17:24:13.795192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Interactions

2025-03-02T17:24:05.313884image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:56.808797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:57.927222image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:58.982628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:00.049974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:01.121603image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:02.108362image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:03.253953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:04.279803image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:05.430987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:56.941192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:58.069186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:59.103448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:00.156413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:01.225229image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:02.224512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:03.367569image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:04.389561image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:05.549971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:57.055657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:58.186290image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:59.223662image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:00.276276image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:01.337868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:02.455249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:03.486516image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:04.505056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:05.669672image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:57.245481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:58.299434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:59.342071image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:00.398465image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:01.446018image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:02.574045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:03.600563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:04.615028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:05.782456image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:57.367364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:58.403838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:59.458300image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:00.515748image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:01.542008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:02.680737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:03.709696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:04.720292image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:05.883101image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:57.469838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:58.508272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:59.580840image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:00.618141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:01.639439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:02.795839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:03.815071image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:04.827857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:05.997834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:57.579533image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:58.625773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:59.699331image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:00.735289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:01.758086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:02.914982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:03.925186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:04.947879image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:06.116802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:57.694808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:58.743552image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:59.819371image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:00.853424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:01.883550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:03.034204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:04.040212image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:05.066739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:06.236025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:57.810543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:58.858717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:23:59.929282image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:01.007803image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:01.993107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:03.142893image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:04.156177image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-02T17:24:05.180616image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2025-03-02T17:24:13.898659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
AgeAverageLeadTimeBookingsCanceledBookingsCheckedInBookingsNoShowedDaysSinceCreationDistributionChannelLodgingRevenueMarketSegmentOtherRevenuePersonsNightsRoomNightsSRAccessibleRoomSRAwayFromElevatorSRBathtubSRCribSRHighFloorSRKingSizeBedSRLowFloorSRMediumFloorSRNearElevatorSRNoAlcoholInMiniBarSRQuietRoomSRShowerSRTwinBed
Age1.0000.2240.0120.1750.0060.1110.0700.1150.1190.2070.1440.1490.0050.0120.0240.3710.0260.0480.0140.0020.0230.0100.0470.0140.134
AverageLeadTime0.2241.000-0.0060.7440.0000.2790.0790.6860.1100.7250.7390.7230.0000.0000.0090.0430.0210.0270.0030.0030.0000.0000.0320.0160.103
BookingsCanceled0.012-0.0061.0000.0650.2720.0210.0530.0290.0450.0270.0310.0410.0000.0050.0000.0000.0000.0160.0000.0380.0000.0000.0000.0000.000
BookingsCheckedIn0.1750.7440.0651.0000.3510.3830.0690.7920.0550.7900.7950.8050.0000.0080.0000.0000.0000.0210.0000.0110.0000.0000.0000.0000.001
BookingsNoShowed0.0060.0000.2720.3511.0000.0110.0640.0920.0650.0200.2630.3140.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.000
DaysSinceCreation0.1110.2790.0210.3830.0111.0000.0620.2370.0700.3070.3140.2990.0110.0250.0280.0550.0570.0690.0080.0050.0020.0140.1320.0110.110
DistributionChannel0.0700.0790.0530.0690.0640.0621.0000.0190.7190.0170.0330.0570.0000.0230.0270.0480.0350.1560.0070.0170.0060.0000.0890.0280.093
LodgingRevenue0.1150.6860.0290.7920.0920.2370.0191.0000.0170.8370.8920.9090.0000.0050.0000.0050.0000.0020.0000.0000.0000.0000.0090.0000.000
MarketSegment0.1190.1100.0450.0550.0650.0700.7190.0171.0000.0150.0450.0510.0000.0320.0320.0600.1080.3390.0170.0190.0060.0060.2150.0310.102
OtherRevenue0.2070.7250.0270.7900.0200.3070.0170.8370.0151.0000.8700.8470.0000.0050.0000.0020.0040.0050.0000.0000.0000.0000.0000.0000.000
PersonsNights0.1440.7390.0310.7950.2630.3140.0330.8920.0450.8701.0000.9520.0000.0100.0130.0120.0110.0300.0000.0000.0000.0000.0210.0050.023
RoomNights0.1490.7230.0410.8050.3140.2990.0570.9090.0510.8470.9521.0000.0000.0000.0080.0000.0000.0140.0000.0000.0000.0000.0000.0000.007
SRAccessibleRoom0.0050.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0070.0000.0820.0000.0000.0210.003
SRAwayFromElevator0.0120.0000.0050.0080.0000.0250.0230.0050.0320.0050.0100.0000.0001.0000.0050.0060.1310.0140.0030.0390.0000.0000.0660.0010.002
SRBathtub0.0240.0090.0000.0000.0000.0280.0270.0000.0320.0000.0130.0080.0000.0051.0000.0280.0280.0280.0090.0000.0000.0000.0100.0000.006
SRCrib0.3710.0430.0000.0000.0000.0550.0480.0050.0600.0020.0120.0000.0000.0060.0281.0000.0000.0410.0000.0000.0000.0100.0060.0000.043
SRHighFloor0.0260.0210.0000.0000.0000.0570.0350.0000.1080.0040.0110.0000.0000.1310.0280.0001.0000.0860.0050.0020.0140.0000.0730.0180.006
SRKingSizeBed0.0480.0270.0160.0210.0120.0690.1560.0020.3390.0050.0300.0140.0000.0140.0280.0410.0861.0000.0080.0000.0000.0040.1060.0170.312
SRLowFloor0.0140.0030.0000.0000.0000.0080.0070.0000.0170.0000.0000.0000.0070.0030.0090.0000.0050.0081.0000.0020.0000.0000.0120.0000.001
SRMediumFloor0.0020.0030.0380.0110.0000.0050.0170.0000.0190.0000.0000.0000.0000.0390.0000.0000.0020.0000.0021.0000.0080.0000.0170.0100.005
SRNearElevator0.0230.0000.0000.0000.0000.0020.0060.0000.0060.0000.0000.0000.0820.0000.0000.0000.0140.0000.0000.0081.0000.0000.0000.0170.000
SRNoAlcoholInMiniBar0.0100.0000.0000.0000.0000.0140.0000.0000.0060.0000.0000.0000.0000.0000.0000.0100.0000.0040.0000.0000.0001.0000.0120.0000.006
SRQuietRoom0.0470.0320.0000.0000.0000.1320.0890.0090.2150.0000.0210.0000.0000.0660.0100.0060.0730.1060.0120.0170.0000.0121.0000.0140.000
SRShower0.0140.0160.0000.0000.0000.0110.0280.0000.0310.0000.0050.0000.0210.0010.0000.0000.0180.0170.0000.0100.0170.0000.0141.0000.000
SRTwinBed0.1340.1030.0000.0010.0000.1100.0930.0000.1020.0000.0230.0070.0030.0020.0060.0430.0060.3120.0010.0050.0000.0060.0000.0001.000

Missing values

2025-03-02T17:24:06.439521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-02T17:24:06.906669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NationalityAgeDaysSinceCreationAverageLeadTimeLodgingRevenueOtherRevenueBookingsCanceledBookingsNoShowedBookingsCheckedInPersonsNightsRoomNightsDistributionChannelMarketSegmentSRHighFloorSRLowFloorSRAccessibleRoomSRMediumFloorSRBathtubSRShowerSRCribSRKingSizeBedSRTwinBedSRNearElevatorSRAwayFromElevatorSRNoAlcoholInMiniBarSRQuietRoom
ID
1PRT52.044059292.082.310264CorporateCorporate0000000000000
2PRT47.0138561280.053.0001105Travel Agent/OperatorTravel Agent/Operator0000000000000
3DEU32.0138500.00.000000Travel Agent/OperatorTravel Agent/Operator0000000000000
4FRA61.0138593240.060.0001105Travel Agent/OperatorTravel Agent/Operator0000000000000
5FRA52.0138500.00.000000Travel Agent/OperatorTravel Agent/Operator0000000000000
6JPN55.0138558230.024.000142Travel Agent/OperatorOther0000000000000
7JPN50.0138500.00.000000Travel Agent/OperatorOther0000000000000
8FRA33.0138538535.094.0001105Travel Agent/OperatorOther0000000100000
9FRA43.0138500.00.000000Travel Agent/OperatorOther0000000100000
10IRL26.0138596174.069.000163Travel Agent/OperatorTravel Agent/Operator0000000000000
NationalityAgeDaysSinceCreationAverageLeadTimeLodgingRevenueOtherRevenueBookingsCanceledBookingsNoShowedBookingsCheckedInPersonsNightsRoomNightsDistributionChannelMarketSegmentSRHighFloorSRLowFloorSRAccessibleRoomSRMediumFloorSRBathtubSRShowerSRCribSRKingSizeBedSRTwinBedSRNearElevatorSRAwayFromElevatorSRNoAlcoholInMiniBarSRQuietRoom
ID
111724ITA56.03700.000.000000Travel Agent/OperatorOther1000000100000
111725ESP60.03743875.00167.8001105Travel Agent/OperatorOther1000000011000
111726PAN60.03700.000.000000Travel Agent/OperatorOther1000000011000
111727PRT51.0377173.5518.000111DirectDirect1000000000001
111728DEU34.0364198.0014.000121Travel Agent/OperatorTravel Agent/Operator0000000100000
111729DEU31.03600.000.000000Travel Agent/OperatorTravel Agent/Operator0000000100000
111730BRA43.036170755.2520.0001105Travel Agent/OperatorOther0000000100000
111731BRA37.03600.000.000000Travel Agent/OperatorOther0000000100000
111732DEU48.03666708.00185.000184Travel Agent/OperatorOther0000000000000
111733DEU48.03600.000.000000Travel Agent/OperatorOther0000000000000

Duplicate rows

Most frequently occurring

NationalityAgeDaysSinceCreationAverageLeadTimeLodgingRevenueOtherRevenueBookingsCanceledBookingsNoShowedBookingsCheckedInPersonsNightsRoomNightsDistributionChannelMarketSegmentSRHighFloorSRLowFloorSRAccessibleRoomSRMediumFloorSRBathtubSRShowerSRCribSRKingSizeBedSRTwinBedSRNearElevatorSRAwayFromElevatorSRNoAlcoholInMiniBarSRQuietRoom# duplicates
2306PRT47.0126810156.044.000121Travel Agent/OperatorGroups000000000000016
2127PRT47.05025515.070.0001105Travel Agent/OperatorGroups000000001000015
2271PRT47.011212783.014.000121DirectDirect000000000000013
2283PRT47.01208192178.018.000122Travel Agent/OperatorGroups000000000000013
2299PRT47.0124419853.014.000121Travel Agent/OperatorGroups000000000000013
2209PRT47.061200.00.000000Travel Agent/OperatorOther00000000000009
2261PRT47.096313469.07.000111Travel Agent/OperatorGroups00000000000009
2282PRT47.0120753159.042.000163Travel Agent/OperatorGroups00000001000009
379DEU55.0494193288.014.000122Travel Agent/OperatorGroups00000000000008
707DEU76.016200.00.000000Travel Agent/OperatorTravel Agent/Operator00000000100008